Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning...
We address the problem of automatically acquiring case frame patterns (selectional patterns) from large corpus data. In particular, we l)ropose a method of learning dependencies b...
—Ad hoc networking will become a major technology for wireless connectivity beside the multimedia necessity within the next few years. There are too many applications where one-t...
In this paper, we present a memetic algorithm with novel local optimizer hybridization strategy for constrained optimization. The developed MA consists of multiple cycles. In each ...